Research On Detecting Complex Network Community Structure

The research on complex network community structure has provided more references for people find more practical significance of the communities. This paper mainly studies the complex network detection algorithm, through learning and research on existing algorithms, and improves an algorithm based on K-means, which detects the community structure of complex networks under the premise of unknown community structure. The algorithm is simple, easy understanding. Using the algorithm in network, the experimental results show that this algorithm is effective. Another reference node density properties, this paper puts forward a method community structure detection algorithms (BSTN) based on similarity between the nodes of the complex network, the algorithm greatly reduce iteration times, using the algorithm in the computer generated stochastic network known community structure, the result shows that this algorithm has higher accuracy than GN algorithm. Also in the actual network, this paper uses karate club network (karate network), the American College Football club network (football network) and the Email network, experimental results compare to Newman algorithm, the proposed algorithm can have less iteration, approximate or more larger value of the module, it shows the BSTN algorithm is effective, and reasonable explain the community structure getting from the BSTN algorithm, the result of from the BSTN algorithm is practical, is reasonable.